71116f07e9
* add model path for hubert * use os.path.join * do inference only if the script is called directly
217 lines
7.1 KiB
Python
217 lines
7.1 KiB
Python
"""
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v1
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runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
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v2
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runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
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"""
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import os, sys, pdb, torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import sys
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import torch
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import tqdm as tq
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from multiprocessing import cpu_count
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class Config:
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def __init__(self, device, is_half):
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self.device = device
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self.is_half = is_half
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self.n_cpu = 0
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self.gpu_name = None
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self.gpu_mem = None
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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def device_config(self) -> tuple:
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if torch.cuda.is_available():
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i_device = int(self.device.split(":")[-1])
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self.gpu_name = torch.cuda.get_device_name(i_device)
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if (
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
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or "P40" in self.gpu_name.upper()
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or "1060" in self.gpu_name
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or "1070" in self.gpu_name
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or "1080" in self.gpu_name
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):
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print("16系/10系显卡和P40强制单精度")
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self.is_half = False
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for config_file in ["32k.json", "40k.json", "48k.json"]:
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with open(f"configs/{config_file}", "r") as f:
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strr = f.read().replace("true", "false")
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with open(f"configs/{config_file}", "w") as f:
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f.write(strr)
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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else:
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self.gpu_name = None
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self.gpu_mem = int(
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torch.cuda.get_device_properties(i_device).total_memory
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/ 1024
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/ 1024
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/ 1024
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+ 0.4
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)
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if self.gpu_mem <= 4:
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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elif torch.backends.mps.is_available():
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print("没有发现支持的N卡, 使用MPS进行推理")
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self.device = "mps"
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else:
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print("没有发现支持的N卡, 使用CPU进行推理")
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self.device = "cpu"
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self.is_half = True
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if self.n_cpu == 0:
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self.n_cpu = cpu_count()
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if self.is_half:
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# 6G显存配置
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x_pad = 3
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x_query = 10
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x_center = 60
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x_max = 65
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else:
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# 5G显存配置
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x_pad = 1
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x_query = 6
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x_center = 38
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x_max = 41
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if self.gpu_mem != None and self.gpu_mem <= 4:
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32
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return x_pad, x_query, x_center, x_max
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f0up_key = sys.argv[1]
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input_path = sys.argv[2]
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index_path = sys.argv[3]
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f0method = sys.argv[4] # harvest or pm
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opt_path = sys.argv[5]
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model_path = sys.argv[6]
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index_rate = float(sys.argv[7])
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device = sys.argv[8]
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is_half = sys.argv[9].lower() != "false"
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filter_radius = int(sys.argv[10])
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resample_sr = int(sys.argv[11])
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rms_mix_rate = float(sys.argv[12])
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protect = float(sys.argv[13])
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print(sys.argv)
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config = Config(device, is_half)
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from vc_infer_pipeline import VC
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from lib.infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from lib.audio import load_audio
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from fairseq import checkpoint_utils
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from scipy.io import wavfile
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hubert_model = None
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def load_hubert(hubert_model_path="hubert_base.pt"):
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global hubert_model
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[hubert_model_path],
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(device)
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if is_half:
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hubert_model = hubert_model.half()
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else:
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hubert_model = hubert_model.float()
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hubert_model.eval()
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def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
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global tgt_sr, net_g, vc, hubert_model, version
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if input_audio is None:
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return "You need to upload an audio", None
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f0_up_key = int(f0_up_key)
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audio = load_audio(input_audio, 16000)
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times = [0, 0, 0]
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if hubert_model == None:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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# audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
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audio_opt = vc.pipeline(
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hubert_model,
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net_g,
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sid,
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audio,
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input_audio,
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times,
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f0_up_key,
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f0_method,
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file_index,
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index_rate,
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if_f0,
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filter_radius,
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tgt_sr,
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resample_sr,
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rms_mix_rate,
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version,
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protect,
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f0_file=f0_file,
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)
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print(times)
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return audio_opt
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def get_vc(model_path):
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global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
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print("loading pth %s" % model_path)
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cpt = torch.load(model_path, map_location="cpu")
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1: #
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净，真奇葩
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net_g.eval().to(device)
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if is_half:
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net_g = net_g.half()
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else:
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net_g = net_g.float()
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vc = VC(tgt_sr, config)
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n_spk = cpt["config"][-3]
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# return {"visible": True,"maximum": n_spk, "__type__": "update"}
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if __name__ == "__main__":
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get_vc(model_path)
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audios = os.listdir(input_path)
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for file in tq.tqdm(audios):
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if file.endswith(".wav"):
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file_path = os.path.join(input_path, file)
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wav_opt = vc_single(
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0, file_path, f0up_key, None, f0method, index_path, index_rate
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)
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out_path = os.path.join(opt_path, file)
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wavfile.write(out_path, tgt_sr, wav_opt)
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